Estimation of Social Distance for COVID19 Prevention using K-Nearest Neighbor Algorithm through deep learning

Coronavirus disease has a crisis with high spread throughout the world during the COVID19 pandemic period. This disease can be easily spread to a group of people and increase the spread. Since it is a worldly disease and not plenty of vaccines available, social distancing is the only best approach to defend against the pandemic situation. All the affected countries' governments declared locked-down to implement social distancing. This social separation and persons not being in a mass group can slow down the spread of COVID19. It reduces the physical contact between infected persons and normal healthy persons. Almost every health organization tells that to follow social distancing people should maintain at least 6 feet of distance from each other. This research proposes a deep learning approach for social distancing which is developed for tracking and detecting people who are in indoor as well as outdoor scenarios using YOLO V3 video analytic technique. This approach focuses to inspect whether the people are maintaining social distancing in many areas, using surveillance video with measuring the distance in real-time performance. Most of the early studies of detecting social distance monitoring were based on GPS for tracking the movements of people where the signals could be lost. On the other hand, some countries use drones to detect large gatherings of people who cannot have a clear view at night times [10]. In the future, the proposed system can be used fully for detecting threats in the public crowded or it can detect any person affected by critical situations (ie fainting, Cordia arrest) or planting the crops in the forms evenly with a uniform measurement. This proposal could be used in many fields like crowd analysis, autonomous vehicles, and human action recognition and could help the government authorities to redesign the public place layout and take precautionary action in the risk zones. This system analyses the social distancing of people by calculating the distance between people to slow downing the spread of the COVID 19 virus. © 2022 IEEE.

An Automated System for Facial Mask Detection and Social Distancing during COVID-19 Pandemic

Since December 2019, the world has started getting affected by a widely spreading virus which we all call the coronavirus. This virus is spread all across the globe, causing many severe health problems and deaths too. COVID-19 is spread when a healthy person comes in contact with the droplets generated when an infected person coughs or sneezes. So, the WHO has suggested some precautionary measures against the spread of this disease. These measures include wearing a mask in public, maintaining social distancing, avoiding mass gatherings. To help reduce the virus’ spread, in this paper, we are proposing a system that detects unmasked people, identifies them, checks if social distancing is followed or not, and also provides a feature of contact tracing. The proposed system consists of mainly two modules: face mask detection and social distancing. There are two more modules which include face recognition and contact tracing. We used two datasets for training our models. First one to detect masks on faces. For this purpose, we collected the image dataset from GitHub and Kaggle. And, the second dataset was for face recognition in which we took our own images for training purposes. It is hoped that our model contributes toward reducing the spread of this disease. Along with COVID-19, this model can also help reduce the spread of similar communicable disease scenarios. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Low-Cost Social Distancing Alerting Wrist Band Device

Covid-19 pandemic is ravaging the world and humankind is facing one of the toughest challenges of this century. The main requirement is to stay safe. According to the Covid-19 protocol, a healthy person who is adjacent to an infected person for more than 15 minutes has a very high chance to get infected. Body temperature more than the normal temperature is one of the symptoms of the symbiotic Covid-19 infected person. This paper presents an idea to design a low-cost affordable wrist band that alerts the user if a person with higher body temperature is in his/her proximity. A simple component like thermopile temperature sensor, amplifier, monostable multivibrator, and LEDs are used to design this wrist band. The paper discusses the outline circuit to achieve this, which can be used by all people. © 2021 IEEE

Uses of Drones in Fighting COVID-19 Pandemic

COVID-19 outbreak has been faced by every country across the globe. Its affects transmit through direct or indirect contact of infected person with a healthy person. So, isolation is one of the mechanism for prevention from it. Now a day's advancement in technology is playing a great role in fighting against COVID-19. Uses of drone is one of them. Drones can offer a large number of services in this era. Drone or UAV's not only minimizes the risk of contact, they had been used for aerial monitoring of containment or curfews areas, for evaluation of post-massive epidemic contagious diseases, for reaching inaccessible areas and many mores. This study, presents the utilization of drones in various areas in fighting against COVID-19 pandemic and various technologies that assists these drones. Various challenges in the implementation of contactless deliveries using drones have been presented. © 2021 IEEE.

Automated Generated Binaural Beats Using EEG: A User-Friendly Auditory Brain-Computer Interface

A person who is physically, mentally, and socially fit is known as a healthy person. However, only 45% of people pass the criteria of being healthy. Approximately 25% of people in India are suffering from mental disorders and are mentally unfit. The current scenario and the lockdowns have increased the rates from 17.5% in March 2020 to 25% in March 2021. Stress has always been an integral part of human life. But with the rising age of digitisation, stress coping abilities are deteriorating, and mental health issues are increasing worldwide, especially among young adults. Mental health has become a significant concern, which needs to be dealt with as soon as possible. According to a survey, many deaths during the covid resulted from anxiety, panic, and mental weakness. Technology is proliferating, but problems like Mental health and cure are still technologically handicapped. Music is known for its healing beauty. It has profound psychophysical effects and can act as a great stress reliever. Integrating music and technology in the right manner can enhance psychological and physical health, which in turn increases brain plasticity. This paper proposes a device that uses non-invasive neurotechnology in acquiring any mental state, from heightened alertness to deep rest. The device uses auditory brain stimulation and neurofeedback technology in achieving so.Binaural Beat possesses brainwave entrainment properties, making it an excellent stimulus in designing this Auditory Brain-Computer Interface. The salient feature of this device is the usage of real-time EEG neurofeedback in producing a personalised binaural beat track for the user. The binaural beat track is structured to help the user smoothly achieve a particular mental state, providing a pleasant experience. Not only that, the experience becomes more harmonious with the integration of the Fibonacci and the Golden Ratio. The device escalates the benefits of binaural beats as the real-time EEG feedback reduces the occurrence of dizziness caused by the beats. Hence, creating an effective system to improve the cognitive functioning of the user. © 2021 IEEE.